Search Results for author: Ivo Danihelka

Found 17 papers, 10 papers with code

Learning by Directional Gradient Descent

no code implementations ICLR 2022 David Silver, Anirudh Goyal, Ivo Danihelka, Matteo Hessel, Hado van Hasselt

How should state be constructed from a sequence of observations, so as to best achieve some objective?

Policy improvement by planning with Gumbel

1 code implementation ICLR 2022 Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver

AlphaZero is a powerful reinforcement learning algorithm based on approximate policy iteration and tree search.

reinforcement-learning

The Cramer Distance as a Solution to Biased Wasserstein Gradients

2 code implementations ICLR 2018 Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos

We show that the Cram\'er distance possesses all three desired properties, combining the best of the Wasserstein and Kullback-Leibler divergences.

Comparison of Maximum Likelihood and GAN-based training of Real NVPs

no code implementations15 May 2017 Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan

We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN.

One-Shot Learning

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

no code implementations NeurIPS 2016 Jack W. Rae, Jonathan J. Hunt, Tim Harley, Ivo Danihelka, Andrew Senior, Greg Wayne, Alex Graves, Timothy P. Lillicrap

SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring $100,\! 000$s of time steps and memories.

Ranked #6 on Question Answering on bAbi (Mean Error Rate metric)

Language Modelling Machine Translation +2

Video Pixel Networks

no code implementations ICML 2017 Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu

The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Video Prediction

Memory-Efficient Backpropagation Through Time

2 code implementations NeurIPS 2016 Audrūnas Gruslys, Remi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves

We propose a novel approach to reduce memory consumption of the backpropagation through time (BPTT) algorithm when training recurrent neural networks (RNNs).

Towards Conceptual Compression

1 code implementation NeurIPS 2016 Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra

We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling.

Ranked #55 on Image Generation on CIFAR-10 (bits/dimension metric)

Image Generation

One-Shot Generalization in Deep Generative Models

no code implementations16 Mar 2016 Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

In particular, humans have an ability for one-shot generalization: an ability to encounter a new concept, understand its structure, and then be able to generate compelling alternative variations of the concept.

Density Estimation Image Generation

Associative Long Short-Term Memory

3 code implementations9 Feb 2016 Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves

We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters.

Grid Long Short-Term Memory

1 code implementation6 Jul 2015 Nal Kalchbrenner, Ivo Danihelka, Alex Graves

This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images.

Language Modelling Translation

DRAW: A Recurrent Neural Network For Image Generation

20 code implementations16 Feb 2015 Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.

Ranked #61 on Image Generation on CIFAR-10 (bits/dimension metric)

Foveation Image Generation

Neural Turing Machines

34 code implementations20 Oct 2014 Alex Graves, Greg Wayne, Ivo Danihelka

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes.

Deep AutoRegressive Networks

no code implementations31 Oct 2013 Karol Gregor, Ivo Danihelka, andriy mnih, Charles Blundell, Daan Wierstra

We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data.

Atari Games

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